Artificial intelligence has and continues to make significant impacts on various sectors. Currently, businesses, healthcare, farming, filmmaking, and many other industries rely on AI-developed tools for various functionalities. Face recognition technology, personalized experiences, wearables, and AI chatbots are just a few innovations that have made artificial intelligence integral to every sector.
However, as it continues its buzz, artificial intelligence wouldn’t be possible without programming languages. Developers use a wide array of coding languages to create AI projects. And while multiple programming languages can be used for AI projects, Python is the most preferred language by developers. Below are the reasons why;
- Great Framework and Library
Choosing a coding language that complements AI algorithms is one of the many challenges developers face when creating AI projects. However, the bottom line is choosing a well-structured language and has the right set of libraries. To ease development, most programmers prefer Python for its wide library and frameworks. Software libraries feature pre-written codes that can help developers solve various programming issues.
Unlike other apps, Artificial intelligence and machine learning apps process data continuously, and Python’s library allows developers to access and transform data. Some of the common Python libraries include;
- NumPy – artificial intelligence projects cannot be completed without NumPy. It is a software package that facilitates scientific computing. The package has a wide library with multi-dimensional objects that work together to reduce the computational complexity of programs.
- SciPy – is another popular Python library that contributes to the completion of AI projects. It is actually the first library of choice for use in projects that involve heavy computing. SciPy library is very user-friendly, thus perfect for beginners.
- Pandas –is an open-source software package that enables developers to manipulate and analyze data. It has excellent data assessment and visualization features.
- Scikit-learn – this library is a combination of SciPy and NumPy. The library is best for both supervised and unsupervised learning, thus great for data mining and analysis projects.
- Keras – it is an API that runs on TensorFlow and allows programmers to experiment with artificial intelligence projects quickly.
- Matplotlib – this is the greatest library with data exploration and visualization capabilities. It has charts, scatterplots, histograms, and more tools that help customize AI projects.
These solutions make it easy for programmers to develop their AI projects faster. However, developers still have to spend some time finding the perfect library for their projects.
- Simple and Consistent
Python language is like English, making the learning process easier. It has a simple syntax that allows users to handle complex systems comfortably and understand how different system elements relate. Since algorithms behind AI can be complicated, the simplicity of this language allows developers to focus on solving the AI problems instead of being worried about getting the technical details of the programming language right.
The ease of understanding makes it simpler to create AI models with Python. Besides, most developers agree that Python is intuitive compared to other programming languages. It can also be used for collaborative implementation, particularly if multiple developers are working on a single project.
- Large Community and Popular
A Stack Overflow survey done in 2018 found that Python is among the popular programming languages developers use globally and the 5th fastest-rising coding language. There are also over 140,000 repositories online with custom-built software Python packages. A language with a strong community is beneficial in many ways, especially with its open-source nature. Developers can share and discuss arising problems on forums to find solutions.
Python is also preferred for AI projects for its great flexibility. Developers can choose between scripting or OOPs programming options. Besides, they don’t have to recompile source codes and can be combined with other programming languages. Python’s flexibility also allows programmers to choose their preferred programming style, which can be;
- Imperative style – has commands that describe how computers should respond to the commands.
- Functional style – also known as declarative programming style as it declares all operations that should be performed. Unlike imperative style, it doesn’t factor in the computer program’s state.
- Object-oriented style – based on objects and classes, where classes are formed by similar objects. This style isn’t supported fully by Python, and programmers rarely use it.
- Procedural style – the best for beginners as it issues step-by-step commands.
Such unlimited flexibility reduces the possibility of encountering errors.
Computing AI projects is tasking, and developers prefer using a simple language that reduces technical baggage. Without a doubt, Python fits all the prerequisites of a good programming language for artificial intelligence projects. That said, aspiring developers looking to tap into the AI market and other purposes should learn Python. They can do so by enrolling in online courses or coding Bootcamps. Service members and veterans can apply for GI Bill Benefits to fund their education.